Online video streaming is continuing its explosive growth. As per Cisco's 2016 Visual Network Index report, mobile video traffic (as of the date of that report) accounts for more than half of all mobile data traffic. Despite a number of recent innovations, ensuring good user Quality of Experience (QoE) over wireless networks such as cellular remains technically challenging, due to the variable network conditions inherent in such environments. Adaptive Bitrate (ABR) streaming (specifically HTTP Live Streaming (HLS) and Dynamic Adaptive Streaming over HTTP (DASH)) has emerged as the de facto streaming technology for dynamically adapting the video streaming quality based on varying network conditions. At the video server, a video is compressed into multiple independent streams (or tracks), each specifying the same content but with different bitrate/quality. A track is further divided into a series of chunks, each containing data for a few seconds' worth of playback. During playback, the adaptation logic at the client determines, for any playback position in the video, which quality chunk to fetch based on the prevailing network conditions. The playback involves a mix of chunks from different tracks for different parts of the video. Due to the extremely high bandwidth requirements, digital video typically has to be compressed before being sent over the network. The video compression used for a track can be: (1) Constant Bitrate (CBR)—attempts to encode the entire video at a relatively fixed bitrate by varying the quantization parameter (and hence the quality) across different scenes; or (2) Variable Bitrate (VBR)—encodes simple scenes (i.e., low-motion or low-complexity scenes) with fewer bits and complex scenes (i.e., high-motion or high-complexity scenes) with more bits, while maintaining a more consistent quality throughout the track. VBR presents some key advantages over CBR: for instance, the ability to realize better video quality for the same average bitrate, or lower bitrate encoding than CBR for the same equivalent quality. Traditionally, only CBR was mainly deployed, partly due to various practical difficulties including the complex encoding pipelines for VBR, as well as the demanding storage, retrieval, and transport challenges posed by the multi-timescale bitrate burstiness of VBR videos. Only fairly recently have content providers begun adopting VBR encoding, spurred by the promise of substantial improvements in the quality-to-bits ratio compared to CBR, and by technological advancements in VBR encoding pipelines.
Two conventional efforts that develop ABR adaptation schemes for VBR videos rely on optimizing traditional QoE metrics defined for CBR videos, and also suffer from performance degradations including large amounts of re-buffering and/or significant viewing quality changes (see Huang, Te-Yuan, et al. “A buffer-based approach to rate adaptation: Evidence from a large video streaming service.” ACM SIGCOMM Computer Communication Review 44.4 (2015): 187-198 and Zhang, Tong, et al. “Modeling and analyzing the influence of chunk size variation on bitrate adaptation in DASH.” INFOCOM 2017-IEEE Conference on Computer Communications, IEEE. IEEE, 2017). The techniques of this Huang reference are sometimes referred to herein as “BBA1” or “BBA-1” and the techniques of this Zhang reference are sometimes referred to herein as “RBA”.